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They are pre-trained as language models, but later can be used in genetic programming or RL to learn from outcomes. They could iterate on problem solving.

Well, the thing is that my friend doesn't pitch ideas to people with money as a job. He's just friends with them and they go out for coffee/ dinner, sometimes they make a deal, etc.. So a "formal approach" for VC funding doesn't apply here.

Truly, a back-of-the-napkin idea won't catch anyone's eyes, that's why I'm searching for someone that can give some feasible milestones/ a timeframe and budgets for them and he will present that.

It sounds like what you're considering the "opposite" is just a reframing of original task i.e. if a token is difficult to predict, then more layers (and therefore compute) would be used used. If it's easy, fewer layers. Am I missing something from what you're asking?

I hope so. I used Cortana. I hate that they removed it. (At least they disabled it for Iceland, while it was active before.)

Indeed.

Yes, just try searching "What is the world record for crossing the English Channel entirely on foot?" and enjoy the litany of unrelated answers, mostly about swimming across.

How would you even do that? Once you have run inference through all layers, you can not just randomly pull additional layers out of thin air, can you?

I’m pretty sure that I will have students who are willing to do this . But just how do you plan on getting the data ? Or is it something the guy you hire would have to sort out .

Also not to be pessimistic but I absolutely do not think it would be possible to make a deep learning model that predicts how many square meters a property is based on some pictures alone . This is a mammoth task .

Of course the code fails at first run. My code fails at first run, too. But I can iterate. If MS allows feedback from the debugger, the model could fix most of its errors.

And when you want to solve a quantitative question the best way is to ask for a Python script that would print the answer when executed.

Copilot is not prompt-tuned, chatGPT would understand new tasks much easier.

You didn't think 175B parameters would make a difference, did you?

What does early stopping inference mean tho

The real question is : can you get accurate data...you need to scan pics for a gazillion different kind of things and in the end provide a number..but in order for this to work you need a ton of data to provide the correct object matches

I'm not even convinced it's possible based on the requirements. You're not going to get structured data. Just pictures of the outside and inside of the house I assume. How are you going to reliably estimate livable space, current state, or even number of rooms when not even all rooms might be properly pictured. You're banking on extracting these features from what I assume to be suboptimal images with high accuracy (very doubtful tbh) and then estimating price based on the features, which is useless if the features aren't extracted properly from the images.

Even if this was possible with high enough accuracy, the dataset you would need for this has be absolutely huge. I really don't believe someone can gather enough in 6 months while simultaneously developing the nn.

And then we're not even talking about the legality of scraping competitors websites to compare them to.

I'm not convinced I could do this in 6 months and I wouldn't do it for that price.

>No, I get what you're looking for VC for
>
>What do you see as the value proposition of made up search engine results?

Doesn't seem like you can decide what you're talking about. Like I said, the VC isn't for made up search results. Now you want to change the subject. Weird.

Thanks for the quote though: https://twitter.com/LatentWeb/status/1613593337918783502

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Ohh I can't imagine how bad the " Cortana, answer the client 's email" can turn out 😂

Alright, that’s fair - edited. I didn’t read through the first link properly.

Point remains that there is pretty generally a pretty complete lack of knowledge of what the field is like. r/ControlProblem most certainly is full of nonsense.

Cherry picking Musk and Hawking out of a list which includes Norvig, Deepmind, Russel and "academics from Cambridge, Oxford, Stanford, Harvard and MIT" is just dishonest.

Diffraction

>Diffraction is defined as the interference or bending of waves around the corners of an obstacle or through an aperture into the region of geometrical shadow of the obstacle/aperture. The diffracting object or aperture effectively becomes a secondary source of the propagating wave. Italian scientist Francesco Maria Grimaldi coined the word diffraction and was the first to record accurate observations of the phenomenon in 1660. In classical physics, the diffraction phenomenon is described by the Huygens–Fresnel principle that treats each point in a propagating wavefront as a collection of individual spherical wavelets.

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Yea, that's no exaggeration. The hardest part is continually remind yourself to keep trying new things to push it further.

I asked GitHub Copilot to write a couple of Python functions, and I was pretty impressed. I dont mean, "write a function to add two numbers" or "parse some text". I defined an Nx3 and an Mx3 Numpy matrices (a & b), told it (in comments) that a & b were two arrays of 3D points. I asked it to write a very fast function to compute all distances between pts in a and b, and return it. It did it immediately and the results were correct, so it's a start.

I had already written my own function to do it with vectorized numpy math (no slow loops), and optimized. I wanted to know which was faster.
The result is always a symmetric MxN matrix and diagonals are zero, so I knew my outer-difference MxN matrix had to be wasteful.

I benchmarked CoPilot's code against my own: their code was 3X faster in large matrix tests.

The second example: I told it I have a laser with 700 nm wavelength. I gave it some specs, like the diameter of the laser, an aperture size, and told CoPilot to write a function to compute and plot the laser image projection on a plane that was X mm away. It did it first try. It looks something like this image:

Yea... amazing is just the start.

I think people are thinking too much into it so much so that even Microsoft doesn't know

Sure but no one can give you any of those things with the back of the napkin idea you've provided. You're a very long way from being able to pitch for funding.

You need a dataset of a few thousands or a few millions examples of input (documents + other contextual info like location data) and outputs ( estimates, other attributes like number of bedrooms and stuff) in order to build such feature. Depending on the quality and amount of data that you have and the perfomance requirements that you have, this can go from a few months projetcs to nearly impossible to do. (note, if you have no data like you said or expect 0 error, then this is impossible to do)